What Youll Do

- Define the technical architecture and overall stack for an agent-native business applications platform spanning pricing guidance, booking and order intelligence, upsell recommendations, churn and retention prediction, media planning, deal scoring, revenue forecasting, and more.
- Evaluate LLMs, multimodal systems, multi-agent orchestration frameworks, and recommendation, ranking, and forecasting models for product use.
- Design and build the pipeline from business and customer signals to model inference, recommendation generation, output validation, and integration with internal revenue systems and APIs.
- Build production-grade systems with solid error handling, output validation, explainability, auditability, and human-in-the-loop guardrails for high-stakes pricing and financial decisions.
- Partner cross-functionally with ML, backend, frontend, data, and business teams to iterate quickly based on feedback and business needs.
- Drive technical decisions that directly influence revenue impact, product quality, scalability, and time-to-market.

Required Qualifications
- Bachelors degree in Computer Science or a related field.
- Experience building recommendation or decisioning systems, ideally in advertising, media, or revenue-platform environments.
- Strong understanding of modern LLMs and agentic systems, and the ability to evaluate latency, cost, and quality tradeoffs.
- Solid experience with LLM and multi-agent pipelines, including prompting, tool use, orchestration, tradeoff analysis, and error handling.
- Experience deploying ML systems in production, including model serving, containerization, CI/CD,



and monitoring.
- Hands-on experience with modern ML frameworks and tooling such as PyTorch, Hugging Face Transformers, agent orchestration frameworks (e.g., LangGraph or similar), feature stores, and vector databases for RAG workflows.
- Experience designing evaluation approaches for recommendation and generative systems using human review, automated and offline metrics, and online A/B testing.
- Strong software engineering fundamentals and solid production experience in Java or Python.
- Ability to translate ambiguous business requirements into practical technical solutions and communicate tradeoffs clearly to cross-functional partners.

Preferred Qualifications
- Startup or founding-engineer experience, or experience leading fast-moving Gen AI or agentic product initiatives.
- Experience owning a business-facing or self-serve product from architecture through production deployment and operational support.
- Deeper background in recommendation systems, pricing optimization, demand forecasting, uplift and churn modelling, or quoting and optimization.
- Experience improving inference cost and latency, and building responsible AI guardrails such as safety, compliance, auditability, and human review mechanisms.
- Open-source contributions or publications in ML, recommender systems, or applied AI for revenue and decisioning.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Software Engineer, Machine Learning (Karnataka)
🏢 Roku
📍 Karnataka